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Data-Driven Interaction Methods for Socially Assistive Robotics:
Validation With Children With Autism Spectrum Disorders
by
David J. Feil-Seifer
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2012
Copyright 2012 David J. Feil-Seifer

There exists a great untapped potential for the use of intelligent robots as therapeutic social partners for children. However, enabling a robot to understand social behavior, and do so while interacting with the child, is a challenging problem. Children are highly individual and thus technology used for social interaction requires recognition of a wide-range of social behavior. This argues for data-driven methods that capture the relevant range of interactions. This work addresses the challenge of designing data-driven behaviors for socially assistive robots in order to enable them to recognize and appropriately respond to a child's free-form behavior in unstructured play contexts. The focus on free-form behavior is inspired by and grounded in the DIR/Floortime approach to therapeutic intervention with children with autism spectrum disorders (ASD). This approach emphasizes fostering engagement through play, recognizing social behavior and using ""engagements"" to bolster social interactions. ❧ This research presents a data-driven methodology and a validated experimental framework for enabling fully autonomous robots to interact with both typically developing children and children with ASD in undirected scenarios using socially appropriate behavior, especially where spatial interaction is concerned. Autonomous robot operation as a critical aspect of the methodology; save for safety interventions by a human operator, the robot acts of its own accord. The robot and child engage in free-form interaction, in part though distance-oriented behaviors; the robot must be able to recognize the child's behaviors and respond to them appropriately. This dissertation presents the following computational contributions with therapeutic potential: ❧ Detection and mitigation of a child's distress: This work presents methodology for learning and then applying a data-driven spatio-temporal model of social behavior based on distance-based features to automatically differentiate between typical vs. aversive child-robot interactions. Using a Gaussian Mixture Model learned over distance-based feature data, the developed system is able to detect and interpret social behavior with sufficient accuracy to recognize child distress. The robot uses this model to change its own behavior so as to encourage positive social interaction. ❧ Encouragement of human-human and human-robot interaction: This work demonstrates a global and local motion planner that uses the above spatio-temporal model as part of the determination of a motion trajectory that maintains the robot's spatial relationship with the child and sustains interaction while also encouraging the child to move toward another proximal interaction partner. The desired spatial interaction behavior is achieved by modifying an established trajectory planner to weight candidate trajectories based on conformity to a trained model of the desired behavior. ❧ Data-Driven Approach For Providing Graded Cueing Feedback: A methodology for robot behavior that provides autonomous feedback for a robot-child imitation and turn-taking game. This is accomplished by incorporating an established therapeutic model of feedback along with a trained model of imitation behavior. This is used as part of an autonomous system that can play a turn-taking game, recognize breeches in imitation behavior, and interpret a breech in order to provide appropriate feedback. The approach is validated in a spatial imitation game, used to gauge the presence of imitation behavior. ❧ The three main contributions above: averse behavior detection, model-based trajectory planning, and data-driven feedback, have been instantiated and validated in several SAR systems using autonomous person sensing, behavior interpretation, and action selection, for the purposes of detecting, provoking, and encouraging both human-human and human-robot social interaction. The validated systems were tested in experiments that evaluated the system design, the accuracy of the robot's ability to interpret observed behavior, the appropriateness of the robot's responses, and the quality of the child-robot and child-parent social behavior interaction. The evaluation experiments were conducted with both children with ASD and typically developing children. The systems were also used to explore the therapeutic potential of socially assistive robots facilitated by the developed models, architecture, and experiment framework.

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Data-Driven Interaction Methods for Socially Assistive Robotics:
Validation With Children With Autism Spectrum Disorders
by
David J. Feil-Seifer
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
May 2012
Copyright 2012 David J. Feil-Seifer